refactor: rag storage refactor (#2434)

docs/docs/installation/integrations/bm25_rag_install.md

@@ -0,0 +1,45 @@
+# BM25 RAG
+
+In this example, we will show how to use the Elasticsearch as in DB-GPT RAG Storage. Using a Elasticsearch database to implement RAG can, to some extent, alleviate the uncertainty and interpretability issues brought about by Elasticsearch database retrieval.
+
+### Install Dependencies
+
+First, you need to install the `dbgpt elasticsearch storage` library.
+
+```bash
+uv sync --all-packages --frozen \
+--extra "base" \
+--extra "proxy_openai" \
+--extra "rag" \
+--extra "storage_elasticsearch" \
+--extra "dbgpts"
+````
+
+### Prepare Elasticsearch
+
+Prepare Elasticsearch database service, reference-[Elasticsearch Installation](https://www.elastic.co/guide/en/elasticsearch/reference/current/install-elasticsearch.html) .
+
+### Elasticsearch Configuration
+
+
+Set rag storage variables below in `configs/dbgpt-bm25-rag.toml` file, let DB-GPT know how to connect to Elasticsearch.
+
+```
+
+[rag.storage]
+[rag.storage.full_text]
+type = "ElasticSearch"
+uri = "127.0.0.1"
+port = "9200"
+```
+
+Then run the following command to start the webserver:
+```bash
+uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-bm25-rag.toml
+```
+
+Optionally
+```bash
+uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/dbgpt-bm25-rag.toml
+```
+

docs/sidebars.js

@@ -107,6 +107,10 @@ const sidebars = {
                   type: "doc",
                   id: "installation/integrations/oceanbase_rag_install"
                 },
+                {
+                  type: "doc",
+                  id: "installation/integrations/bm25_rag_install"
+                },
                 {
                   type: "doc",
                   id: "installation/integrations/milvus_rag_install"